Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks

Aims and background Pests and diseases of plants often threaten the availability and safety of plants for human consumption. To face these challenges, a new agricultural revolution is underway (agriculture 4.0). This agrarian revolution dramatically benefits from new digital technologies and artificial intelligence (AI). Methods The farmers need a reliable tool for an early disease diagnosis. Imaging is a promising technique for diagnosing and quantifying the disease plot. Easily automated and non-intrusive, imaging allows, with low costs in instrumentation and human resources, to account for much agricultural priority's local mics on large production areas. The main purpose paper is to develop a hybrid model for tomato disease detection based on image data collection. We apply transfer learning and fine-tuning strategies to improve the performance of different pre-trained models. Two models have been selected to develop our hybrid model for plant disease identification among these CNN models. We used the plant village dataset, which contains nine classes of tomato diseases. Results First, we evaluate the performance of seven different architectures including VGG16, ResNet50, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3 and EfficientNetB4. We applied the transfer learning technique. Then, the best two pre-trained models were selected and used to implement a weighted average ensemble. The proposed model achieves an accuracy of 0.981. Conclusion Many diseases can affect tomato plants and cause yield losses. Therefore, plant pathogens should be given more importance. Furthermore, this study can be adapted to cover other types of crops in future research.